MODEL_PARAMS = {
    # Type of model that the rest of these parameters apply to.
    'model': "CLA",

    # Version that specifies the format of the config.
    'version': 1,

    # Intermediate variables used to compute fields in modelParams and also
    # referenced from the control section.
    'predictAheadTime': None,

    # Model parameter dictionary.
    'modelParams': {
        # The type of inference that this model will perform
        'inferenceType': 'TemporalAnomaly',

        'sensorParams': {
            # Sensor diagnostic output verbosity control;
            # if > 0: sensor region will print out on screen what it's sensing
            # at each step 0: silent; >=1: some info; >=2: more info;
            # >=3: even more info (see compute() in py/regions/RecordSensor.py)
            'verbosity' : 0,

            # Example:
            #     dsEncoderSchema = [
            #       DeferredDictLookup('__field_name_encoder'),
            #     ],
            #
            # (value generated from DS_ENCODER_SCHEMA)
            'encoders': {
                u'vector':    {
                    'fieldname': u'vector',
                    'n': 2048,
                    'w': 51,
                    'name': u'vector',
                    'type': 'CoordinateEncoder'},
                u'timestamp_dayOfWeek': {
                    'dayOfWeek': (21, 1.09375),
                    'fieldname': 'timestamp',
                    'name': 'timestamp',
                    'type': 'DateEncoder'},
            },

            # A dictionary specifying the period for automatically-generated
            # resets from a RecordSensor;
            #
            # None = disable automatically-generated resets (also disabled if
            # all of the specified values evaluate to 0).
            # Valid keys is the desired combination of the following:
            #  days, hours, minutes, seconds, milliseconds, microseconds, weeks
            #
            # Example for 1.5 days: sensorAutoReset = dict(days=1,hours=12),
            'sensorAutoReset' : None,
        },

        'spEnable': True,

        'spParams': {
            # SP diagnostic output verbosity control;
            # 0: silent; >=1: some info; >=2: more info;
            'spVerbosity' : 0,

            # Spatial Pooler implementation selector.
            # Options: 'py', 'cpp' (speed optimized, new)
            'spatialImp' : 'cpp', 

            'globalInhibition': 1,

            # Number of columns in the SP (must be same as in TP)
            'columnCount': 2048,

            'inputWidth': 0,

            # SP inhibition control (absolute value);
            # Maximum number of active columns in the SP region's output (when
            # there are more, the weaker ones are suppressed)
            'numActiveColumnsPerInhArea': 40,

            'seed': 1956,

            # potentialPct
            # What percent of the columns's receptive field is available
            # for potential synapses. 
            'potentialPct': 0.8,

            # The default connected threshold. Any synapse whose
            # permanence value is above the connected threshold is
            # a "connected synapse", meaning it can contribute to the
            # cell's firing. Typical value is 0.10. 
            'synPermConnected': 0.1,

            'synPermActiveInc': 0.0001,

            'synPermInactiveDec': 0.0005,

            'maxBoost': 1.0,
        },

        # Controls whether TP is enabled or disabled;
        # TP is necessary for making temporal predictions, such as predicting
        # the next inputs.  Without TP, the model is only capable of
        # reconstructing missing sensor inputs (via SP).
        'tpEnable' : True,

        'tpParams': {
            # TP diagnostic output verbosity control;
            # 0: silent; [1..6]: increasing levels of verbosity
            # (see verbosity in nta/trunk/py/nupic/research/TP.py and TP10X*.py)
            'verbosity': 0,

            # Number of cell columns in the cortical region (same number for
            # SP and TP)
            # (see also tpNCellsPerCol)
            'columnCount': 2048,

            # The number of cells (i.e., states), allocated per column.
            'cellsPerColumn': 32,

            'inputWidth': 2048,

            'seed': 1960,

            # Temporal Pooler implementation selector (see _getTPClass in
            # CLARegion.py).
            'temporalImp': 'cpp',

            # New Synapse formation count
            # NOTE: If None, use spNumActivePerInhArea
            #
            # TODO: need better explanation
            'newSynapseCount': 20,

            # Maximum number of synapses per segment
            #  > 0 for fixed-size CLA
            # -1 for non-fixed-size CLA
            #
            # TODO: for Ron: once the appropriate value is placed in TP
            # constructor, see if we should eliminate this parameter from
            # description.py.
            'maxSynapsesPerSegment': 32,

            # Maximum number of segments per cell
            #  > 0 for fixed-size CLA
            # -1 for non-fixed-size CLA
            #
            # TODO: for Ron: once the appropriate value is placed in TP
            # constructor, see if we should eliminate this parameter from
            # description.py.
            'maxSegmentsPerCell': 128,

            # Initial Permanence
            # TODO: need better explanation
            'initialPerm': 0.21,

            # Connected Permanence
            'connectedPerm': 0.5,

            # Permanence Increment
            'permanenceInc': 0.1,

            # Permanence Decrement
            # If set to None, will automatically default to tpPermanenceInc
            # value.
            'permanenceDec' : 0.1,

            'globalDecay': 0.0,

            'maxAge': 0,

            # Minimum number of active synapses for a segment to be considered
            # during search for the best-matching segments.
            # None=use default
            # Replaces: tpMinThreshold
            'minThreshold': 3,

            # Segment activation threshold.
            # A segment is active if it has >= tpSegmentActivationThreshold
            # connected synapses that are active due to infActiveState
            # None=use default
            # Replaces: tpActivationThreshold
            'activationThreshold': 6,

            'outputType': 'normal',

            # "Pay Attention Mode" length. This tells the TP how many new
            # elements to append to the end of a learned sequence at a time.
            # Smaller values are better for datasets with short sequences,
            # higher values are better for datasets with long sequences.
            'pamLength': 3,
        },

        # Don't create the classifier since we don't need predictions.
        'clEnable': False,
        'clParams': None,

        'anomalyParams': {  u'anomalyCacheRecords': None,
        u'autoDetectThreshold': None,
        u'autoDetectWaitRecords': 2184},

        'trainSPNetOnlyIfRequested': False,
    },
}

